Exploration of COVID-19 tracking data from multiple resources
Wei Sun
2020-04-29
1 Introduction
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a new type of coronavirus: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak first started in Wuhan, China in December 2019. The first kown case of COVID-19 in the U.S. was confirmed on January 20, 2020, in a 35-year-old man who teturned to Washington State on January 15 after traveling to Wuhan. Starting around the end of Feburary, evidence emerge for community spread in the US.
We, as all of us, are indebted to the heros who fight COVID-19 across the whole world in different ways. For this data exploration, I am grateful to many data science groups who have collected detailed COVID-19 outbreak data, including the number of tests, confirmed cases, and deaths, across countries/regions, states/provnices (administrative division level 1, or admin1), and counties (admin2). Specifically, I used the data from these three resources:
JHU (https://coronavirus.jhu.edu/)
The Center for Systems Science and Engineering (CSSE) at John Hopkins University.
World-wide counts of coronavirus cases, deaths, and recovered ones.
NY Times (https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html)
The New York Times
``cumulative counts of coronavirus cases in the United States, at the state and county level, over time’’
COVID Trackng (https://covidtracking.com/)
COVID Tracking Project
``collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data’’
2 JHU
Assume you have cloned the JHU Github repository on your local machine at ``../COVID-19’’.
2.1 time series data
The time series provide counts (e.g., confirmed cases, deaths) starting from Jan 22nd, 2020 for 253 locations. Currently there is no data of individual US state in these time series data files.
Here is the list of 10 records with the largest number of cases or deaths on the most recent date.
Next, I check for each country/region, what is the number of new cases/deaths? This data is important to understand what is the trend under different situations, e.g., population density, social distance policies etc. Here I checked the top 10 countries/regions with the highest number of deaths.
2.2 daily reports data
The raw data from Hopkins are in the format of daily reports with one file per day. More recent files (since March 22nd) inlcude information from individual states of US or individual counties, as shown in the following figure. So I turn to NY Times data for informatoin of individual states or counties.
3 NY Times
The data from NY Times are saved in two text files, one for state level information and the other one for county level information.
The currente date is
## [1] "2020-04-27"3.1 state level data
First check the 20 states with the largest number of deaths.
## date state fips cases deaths
## 3073 2020-04-27 New York 36 292027 17303
## 3071 2020-04-27 New Jersey 34 111188 6044
## 3063 2020-04-27 Michigan 26 38190 3406
## 3062 2020-04-27 Massachusetts 25 56462 3003
## 3046 2020-04-27 Connecticut 9 25997 2012
## 3054 2020-04-27 Illinois 17 45883 1992
## 3080 2020-04-27 Pennsylvania 42 43728 1946
## 3044 2020-04-27 California 6 45208 1800
## 3059 2020-04-27 Louisiana 22 27068 1697
## 3049 2020-04-27 Florida 12 32130 1087
## 3050 2020-04-27 Georgia 13 23229 981
## 3061 2020-04-27 Maryland 24 19487 858
## 3055 2020-04-27 Indiana 18 15961 844
## 3091 2020-04-27 Washington 53 13864 771
## 3077 2020-04-27 Ohio 39 16325 753
## 3045 2020-04-27 Colorado 8 13804 705
## 3086 2020-04-27 Texas 48 25960 699
## 3090 2020-04-27 Virginia 51 13535 458
## 3074 2020-04-27 North Carolina 37 9142 331
## 3066 2020-04-27 Missouri 29 7171 296For these 20 states, I check the number of new cases and the number of new deaths. Part of the reason for such checking is to identify whether there is any similarity on such patterns. For example, could you use the pattern seen from Italy to predict what happen in an individual state, and what are the similarities and differences across states.
Next I check the relation between the cumulative number of cases and deaths for these 10 states, starting on March
3.2 county level data
First check the 20 counties with the largest number of deaths.
## date county state fips cases deaths
## 94265 2020-04-27 New York City New York NA 160499 11857
## 94264 2020-04-27 Nassau New York 36059 34865 2003
## 93812 2020-04-27 Wayne Michigan 26163 15872 1622
## 93163 2020-04-27 Cook Illinois 17031 31953 1347
## 94284 2020-04-27 Suffolk New York 36103 32470 1147
## 94292 2020-04-27 Westchester New York 36119 28007 1077
## 94191 2020-04-27 Essex New Jersey 34013 13047 1028
## 94186 2020-04-27 Bergen New Jersey 34003 15104 960
## 92777 2020-04-27 Los Angeles California 6037 20417 942
## 92871 2020-04-27 Fairfield Connecticut 9001 10763 727
## 93727 2020-04-27 Middlesex Massachusetts 25017 12953 700
## 94193 2020-04-27 Hudson New Jersey 34017 13925 673
## 93793 2020-04-27 Oakland Michigan 26125 6913 631
## 92872 2020-04-27 Hartford Connecticut 9003 5157 612
## 94204 2020-04-27 Union New Jersey 34039 12011 583
## 93780 2020-04-27 Macomb Michigan 26099 5245 527
## 94664 2020-04-27 Philadelphia Pennsylvania 42101 12868 484
## 92875 2020-04-27 New Haven Connecticut 9009 6993 456
## 94196 2020-04-27 Middlesex New Jersey 34023 10767 455
## 93731 2020-04-27 Suffolk Massachusetts 25025 11883 448For these 20 counties, I check the number of new cases and the number of new deaths.
4 COVID Trackng
The positive rates of testing can be an indicator on how much the COVID-19 has spread. However, they are more noisy data since the negative testing resutls are often not reported and the tests are almost surely taken on a non-representative random sample of the population. The COVID traking project proides a grade per state: ``If you are calculating positive rates, it should only be with states that have an A grade. And be careful going back in time because almost all the states have changed their level of reporting at different times.’’ (https://covidtracking.com/about-tracker/). The data are also availalbe for both counties and states, here I only look at state level data.
Since the daily postive rate can fluctuate a lot, here I only illustrae the cumulative positave rate across time, for four states with grade A data. Of course since this is an R markdown file, you can modify the source code and check for other states.
5 Session information
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] httr_1.4.1 ggpubr_0.2.5 magrittr_1.5 ggplot2_3.2.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.3 pillar_1.4.3 compiler_3.6.2 tools_3.6.2
## [5] digest_0.6.23 evaluate_0.14 lifecycle_0.1.0 tibble_2.1.3
## [9] gtable_0.3.0 pkgconfig_2.0.3 rlang_0.4.4 yaml_2.2.1
## [13] xfun_0.12 gridExtra_2.3 withr_2.1.2 dplyr_0.8.4
## [17] stringr_1.4.0 knitr_1.28 grid_3.6.2 tidyselect_1.0.0
## [21] cowplot_1.0.0 glue_1.3.1 R6_2.4.1 rmarkdown_2.1
## [25] purrr_0.3.3 farver_2.0.3 scales_1.1.0 htmltools_0.4.0
## [29] assertthat_0.2.1 colorspace_1.4-1 ggsignif_0.6.0 labeling_0.3
## [33] stringi_1.4.5 lazyeval_0.2.2 munsell_0.5.0 crayon_1.3.4